Search results for: neural perception.

Authors: Jie Zhao, Meng Su

Abstract:

Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

Procedia PDF Downloads 53

Authors: Abdolreza Memari

Abstract:

In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

Procedia PDF Downloads 460

Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

Abstract:

Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

Procedia PDF Downloads 568

Authors: Oluwaponmile David Alao

Abstract:

Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

Procedia PDF Downloads 621

Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

Abstract:

The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

Procedia PDF Downloads 8

Authors: Vinai K. Singh

Abstract:

In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

Procedia PDF Downloads 106

Authors: Evgeny Kozubenko, Dmitry Shaposhnikov, Mikhail Petrushan

Abstract:

This work is focused on the study of the binocular characteristics of the phenomenon of perisaccadic suppression in humans when perceiving visual objects. This phenomenon manifests in a decrease in the subject's ability to perceive visual information during saccades, which play an important role in purpose-driven behavior and visual perception. It was shown that the impairment of perception of visual information in the post-saccadic time window is stronger (p < 0.05) in the ipsilateral eye (the eye towards which the saccade occurs). In addition, the observed heterogeneity of post-saccadic suppression in the contralateral and ipsilateral eyes may relate to depth perception. Taking the studied phenomenon into account is important when developing ergonomic control panels in modern operator systems.

Keywords: eye movement, natural vision, saccadic suppression, visual perception

Procedia PDF Downloads 125

Authors: Xiaohan Bookman, Xiaoyan Zhu

Abstract:

The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

Procedia PDF Downloads 407

Authors: Sadiq J. Abou-Loukh

Abstract:

The present work deals with implementation of Petri nets, which own the perfect ability of modeling, are used to establish a fault diagnosis model. Fault diagnosis of a control system received considerable attention in the last decades. The formalism of representing neural networks based on Petri nets has been presented. Neural Petri Net (NPN) reasoning model is investigated and developed for the fault diagnosis process of electric control system. The proposed NPN has the characteristics of easy establishment and high efficiency, and fault status within the system can be described clearly when compared with traditional testing methods. The proposed system is tested and the simulation results are given. The implementation explains the advantages of using NPN method and can be used as a guide for different online applications.

Keywords: petri net, neural petri net, electric control system, fault diagnosis

Procedia PDF Downloads 440

Authors: V. Biasi, A. M. Ciraci, G. Domenici, N. Patrizi

Abstract:

We present two studies centered on the teacher’s perception of self-efficacy and professional competences. The first study aims to evaluate the levels of self-efficacy as attitude in 200 teachers of primary and secondary schools. Teacher self-efficacy is related to many educational outcomes: such as teachers’ persistence, enthusiasm, commitment and instructional behavior. High level of teacher self-efficacy beliefs enhance student motivation and pupil’s learning level. On this theoretical and empirical basis we are planning a second study oriented to assess teacher self-perception of competences that are linked to teacher self-efficacy. With the CDVR Questionnaire, 287 teachers graduated in Education Sciences in e-learning mode, showed an increase in their self-perception of didactic-evaluation and relational competences and an increased confidence also in their own professionalism.

Keywords: teacher competence, teacher self-efficacy, selfperception, self-report evaluation

Procedia PDF Downloads 487

Authors: Firas M. Tuaimah

Abstract:

This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

Procedia PDF Downloads 428

Authors: Ya-Fen Lee, Yun-Yao Chi

Abstract:

The study aims to explore the relationship between risk perceptions of rockfall and revisit intention using a Structural Equation Modelling (SEM) analysis. A total of 573 valid questionnaires are collected from travelers to Taroko National Park, Taiwan. The findings show the majority of travellers have the medium perception of rockfall risk, and are willing to revisit the Taroko National Park. The revisit intention to Taroko National Park is influenced by hazardous preferences, willingness-to-pay, obstruction and attraction. The risk perception has an indirect effect on revisit intention through influencing willingness-to-pay. The study results can be a reference for mitigation the rockfall disaster.

Keywords: risk perception, rockfall, revisit intention, structural equation modelling

Procedia PDF Downloads 403

Authors: Osalina Toemapa, Rika Susanti, Husna Yetti

Abstract:

In criminal offense case, such as homicide, investigators may request for an autopsy to the victim without family approval in Indonesia. Generally, there has been decreasing in autopsy rate in the world over past years. Family’s refusal is one of the most common problems. The purpose of this study is to find the correlation between knowledge level and public perception of autopsy on criminal offense victim. This cross-sectional study was done from April to May 2017 in subdistrict Pulau Punjung. Participants were asked to fill the questionnaire. There are 15 questions to asses knowledge level, perception, and factors influencing autopsy refusal. The chi-square test was used for the statistical analysis. Out of the total of 436 respondents, 54,5% were found to have poor knowledge level, and 56,7% were found to have poor perception. There was a significant correlation between knowledge level and public perception (p<0,001). There are 153 respondents who decline autopsy on criminal offense victim with the most factors influencing autopsy refusal is delays in victim’s funeral (92,2%). Conclusion, knowledge level is correlated with public perception in subdistrict of Pulau Punjung, district of Dharmasraya, West Sumatra, Indonesia. Most influencing factor in autopsy refusal is delays in victim’s funeral.

Keywords: knowledge level, public perception, autopsy on criminal offense victim, autopsy refusal

Procedia PDF Downloads 280

Authors: Rehab Abdulmajed, Amr Hamada, Ahmed Elsaid, Hisashi Hayakawa, Ayman Mahrous

Abstract:

Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of solar wind using mathematical models, MHD models, and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulates the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar cycles (sc) 21, 22, 23, and most of 24.

Keywords: artificial neural network, coronal hole area, feed-forward neural network models, solar high speed streams

Procedia PDF Downloads 56

Authors: A. Hadj Taieb, H. Ennouri

Abstract:

The current study aimed to investigate the effect that optical illusion garments have on a woman’s self-perception of her own body shape. First, we created different optical illusion garment by using color contrasts. Second, a short survey based on visual perception is addressed to women in order to compare the different optical illusion garments to determine if they met the established 'ideal' body shape. A ‘visual analysis method’ was used to investigate the clothing models with optical illusions. The theories in relation with the optical illusion were used through this method. The effects of the optical illusion of color contrast on body shape in the fashion sector were tried to be revealed.

Keywords: optical illusion, color contrasts, body image perception, self-esteem

Procedia PDF Downloads 249

Authors: Dung V. H. Pham, Kathryn Cullen

Abstract:

Introduction: 40% of adolescents with major depression (MDD) are unresponsive to 1st line antidepressant treatment. The neural mechanism underlying treatment-responsive and treatment-resistant depression in adolescent are unclear. Amygdala is important for emotion processing and has been implicated in mood disorders. Past research has shown abnormal amygdala connectivity in adolescents with MDD. This research study changes in amygdala resting-state functional connectivity to find neural correlates of successful antidepressant treatment. Methods: Thirteen adolescents aged 12-19 underwent rfMRI before and after 8-week antidepressant treatment and completed BDI-II at each scan. A whole-brain approach, using anatomically defined amygdala ROIs (1) identified brain regions that are highly synchronous with the amygdala, (2) correlated neural changes with changes in overall depression and specific symptom clusters within depression. Results: Some neural correlates were common across domains: (1) decreased amygdala RSFC with the default mode network (posterior cingulate, precuneus) is associated with improvement in overall depression and many symptom clusters, (2) increased amygdala RSFC with fusiform gyrus is associated with symptom improvement across many symptom clusters. We also found unique neural changes associated with symptom improvement in each symptom cluster. Conclusion: This is the first preliminary study that looks at neural correlates of antidepressant treatment response to overall depression as well as different clusters of symptoms of depression. The finding suggests both overlapping and distinct neural mechanisms underlying improvement in each symptom clusters within depression. Some brain regions found are also implicated in MDD among adults in previous literature.

Keywords: depression, adolescents, fMRI, antidepressants

Procedia PDF Downloads 231

Authors: Nurul Bin Ibrahim

Abstract:

Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields.

Keywords: neural networks, machine learning, computational fluid dynamics, stochastic systems, simulation, stratified turbulence

Procedia PDF Downloads 41

Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

Abstract:

The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

Procedia PDF Downloads 170

Authors: S. O. Odebode, O. E. Adelakun

Abstract:

The study assessed the perception of rural dwellers on the nutritional and social values of mushroom in Oyo state, Nigeria. One hundred and forty respondents were sampled and interviewed. Results showed that majority (84.9%) had below secondary education and 81.4 % were married, with fairly large (71.4%) household size (5–8). Also, 52.1% had favourable perception toward the nutritional values and 57.1% toward the social values of mushrooms in the study area. There was significant relationship between sex (X² = 4.673), educational level (X² = 11.969) and perception on the values of mushroom. The knowledge level of respondents on mushroom utilization and production is low in the study area. Farmers’ research field should be established to educate rural dwellers on mushroom farm practices and utilization.

Keywords: mushroom, nutritional value, rural dwellers, social value

Procedia PDF Downloads 277

Authors: Gilang Maulana, Dhika Rahma Qomariah, Yasin Fadil

Abstract:

Purpose: This research meant to measure the magnitude of the influence of the quality of the presentation to the targeted audience perception in catching information presentation. Design/Methodology/Approach: This research uses a quantitative research method. The kind of data that uses in this research is the primary data. The population in this research are students the economics faculty of Semarang State University. The sampling techniques uses in this research is purposive sampling. The retrieving data uses questionnaire on 30 respondents. The data analysis uses descriptive analysis. Result: The quality of presentation influential positive against perception of the audience. This proved that the more qualified presentation will increase the perception of the audience. Limitation: Respondents were limited to only 30 people.

Keywords: quality of presentation, presentation, audience, perception, semarang state university

Procedia PDF Downloads 355

Authors: Nopadol Burananuth

Abstract:

The purposes of this research paper were to study the relationship between the economic factor and political factor, the relationship between political and economic factor and social factor, and the effects of economic factor, political factor, and social factor to the people’s perception about ASEAN Economic Community (AEC). A total of 400 samples were selected from four sub-districts from Arunyaprathet District, Srakaow Province. Data analysis method included multiple regression analysis. The findings revealed that political factor depended on trade cooperation, transportation cooperation, and communication cooperation. Social factor was depended on disaster protection, terrorism protection, and international relations. In addition, the people’s perception of the AEC depended on disaster perception, terrorism protection, international relations, transportation cooperation, communication cooperation, interdependence, and labor movement.

Keywords: economic factors, perception, political factors, social factors

Procedia PDF Downloads 554

Authors: Pavan K. Rallabandi, Kailash C. Patidar

Abstract:

In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

Procedia PDF Downloads 355

Authors: Pierre Angelo Alino, Rafael Alejandro Ang, Maria Carmela Espanol, Dominic Gerard Ferreol, Jendrietch Adrian Lopez

Abstract:

This study aims to explore the differences in perception between Generation X and Generation Z towards mental health and mental health illnesses. Through this study, the researchers seek to identify and explore the differences that exist in the generational perception and determine the possible factors that influence the difference in perception. In order to achieve this, we conducted two focus group discussions (FGD), one composed of Generation X and the other composed of Generation Z. Participants for both focus group discussions were recruited through purposive sampling and online recruitment methods. In these discussions, they were asked questions relating to their personal history, experiences with mental health, and related illnesses, as well as their opinions regarding the subject matter. Afterwhich, we analyzed our data through a thematic analysis. Our study’s findings indicate notable differences in the perception of mental health as well as mental illness between the members of Generations X and Z. Additionally, factors such as culture, personal history, and intimate relationships influence the perceptions of mental health between generation groups.

Keywords: generational difference, mental health, mental health illness, perception

Procedia PDF Downloads 161

Authors: Seyed-Ali Sadegh-Zadeh, Mahboobe Bahrami, Sahar Ahmadi, Seyed-Yaser Mousavi, Hamed Atashbar, Amir M. Hajiyavand

Abstract:

This paper presents an investigation into the concept of neural reshaping, which is crucial for achieving strong artificial intelligence through the development of AI algorithms with very high plasticity. By examining the plasticity of both human and artificial neural networks, the study uncovers groundbreaking insights into how these systems adapt to new experiences and situations, ultimately highlighting the potential for creating advanced AI systems that closely mimic human intelligence. The uniqueness of this paper lies in its comprehensive analysis of the neural reshaping process in both human and artificial intelligence systems. This comparative approach enables a deeper understanding of the fundamental principles of neural plasticity, thus shedding light on the limitations and untapped potential of both human and AI learning capabilities. By emphasizing the importance of neural reshaping in the quest for strong AI, the study underscores the need for developing AI algorithms with exceptional adaptability and plasticity. The paper's findings have significant implications for the future of AI research and development. By identifying the core principles of neural reshaping, this research can guide the design of next-generation AI technologies that can enhance human and artificial intelligence alike. These advancements will be instrumental in creating a new era of AI systems with unparalleled capabilities, paving the way for improved decision-making, problem-solving, and overall cognitive performance. In conclusion, this paper makes a substantial contribution by investigating the concept of neural reshaping and its importance for achieving strong AI. Through its in-depth exploration of neural plasticity in both human and artificial neural networks, the study unveils vital insights that can inform the development of innovative AI technologies with high adaptability and potential for enhancing human and AI capabilities alike.

Keywords: neural plasticity, brain adaptation, artificial intelligence, learning, cognitive reshaping

Procedia PDF Downloads 17

Authors: Suwannee Sroisong, Angkhana Ruenkon, Ronnaphop Eimtab

Abstract:

The purpose of this study was twofold: First, to examine the relationship of the Readiness on the License Examination Testing (RLET) with factors namely achieved motivation, attitude on testing, self-perception, perception in testing among the nursing students at Baromarajonani College of Nursing, Buddhachinaraj, Thailand (BCNB); and secondly, to investigate the factors affecting the RLET of the nursing students. All data were collected from a set of 214 questionnaires of nursing students, second semester and in academic year 2010, at BCNB. As a set of variables in the questionnaire, it consisted of factors of readiness in testing, achieved motivation, attitude on testing, self-perception, and perception in testing. The following statistics were analyzed: frequency, percentage, means, standard deviation, and Stepwise-multiple regression correlation. Research results were as follows: 1) For the relationship among following factors, namely achieved motivation, attitude on testing, self-perception, perception in testing, there were positive correlation coefficients between .324 to .560 at the .05 level of significance; and 2) One crucial factor affecting the RLET of nursing students, namely achieved motivation, was found. The achieved motivation factor could explain the variance or predict the RLET of nursing students at 31.40 percent and at the .05 level of significance.

Keywords: readiness, nursing, license examination testing, Thailand

Procedia PDF Downloads 371

Authors: Supaporn Prajongjai, Pannarungsri Inpayoung

Abstract:

This research attempts to explore the perception and satisfaction of international tourists toward Thai economy, politics and Bangkok attributes. Due to tourism industry provides a high rate of revenue for Thailand, and the outcome from this business drives every section of Thailand such as business, residents’ living level. Unfortunately, some incidents in the country, such as some turmoil, have ruined the city’s image which obviously impacts to the tourism industry, the major source of revenue. The size of this research was 400 international tourists who visit Bangkok, Thailand during the 1st – 20th March 2009 and age between 20 – 65 years. The results reveal that tourists satisfy with all of Bangkok’s attributes including general attractions, heritage attraction, maintenance factors and cultural attraction. Also, tourists’ perception toward Thai politics is significantly related to their satisfaction of Bangkok’s attributes, but their perception toward Thai economy is not significantly correlated to their satisfaction of Bangkok’s attributes.

Keywords: international tourists' perception, tourists' satisfactions, Thai economy, tourism destination

Procedia PDF Downloads 228

Authors: Rami El-Hajj Mohamad, Mahmoud Skafi, Ali Massoud Haidar

Abstract:

Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data.

Keywords: recurrent neural networks, global solar radiation, multi-layer perceptron, gradient, root mean square error

Procedia PDF Downloads 414

Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar

Abstract:

Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

Procedia PDF Downloads 491

Authors: Dalia Rimawi, Walid Salameh, Amal Al-Omari, Hadeel AbdelKhaleq

Abstract:

Predication of Survival time of patients with cancer, is a core factor that influences oncologist decisions in different aspects; such as offered treatment plans, patients’ quality of life and medications development. For a long time proportional hazards Cox regression (ph. Cox) was and still the most well-known statistical method to predict survival outcome. But due to the revolution of data sciences; new predication models were employed and proved to be more flexible and provided higher accuracy in that type of studies. Artificial neural network is one of those models that is suitable to handle time to event predication. In this study we aim to compare ph Cox regression with artificial neural network method according to data handling and Accuracy of each model.

Keywords: Cox regression, neural networks, survival, cancer.

Procedia PDF Downloads 159

Authors: L. Parisi

Abstract:

In this research work, neural networks were applied to classify two types of hip joint implants based on the relative hip joint implant side speed and three components of each ground reaction force. The condition of walking gait at normal velocity was used and carried out with each of the two hip joint implants assessed. Ground reaction forces’ kinetic temporal changes were considered in the first approach followed but discarded in the second one. Ground reaction force components were obtained from eighteen patients under such gait condition, half of which had a hip implant type I-II, whilst the other half had the hip implant, defined as type III by Orthoload®. After pre-processing raw gait kinetic data and selecting the time frames needed for the analysis, the ground reaction force components were used to train a MLP neural network, which learnt to distinguish the two hip joint implants in the abovementioned condition. Further to training, unknown hip implant side and ground reaction force components were presented to the neural networks, which assigned those features into the right class with a reasonably high accuracy for the hip implant type I-II and the type III. The results suggest that neural networks could be successfully applied in the performance assessment of hip joint implants.

Keywords: kinemic gait data, neural networks, hip joint implant, hip arthroplasty, rehabilitation engineering

Procedia PDF Downloads 326